x̄ - > Game Week 2 English Premier League Analysis Using R programming language

Analysis Using R programming language

 **Title: Game Week 2 English Premier League Analysis Using R Code**

**Introduction:**

Welcome back, football enthusiasts! The English Premier League is in full swing, and we're here to break down the exciting action from Game Week 2. In this blog post, we'll delve into the world of sports analytics using the power of R code to analyze key performance metrics, trends, and standout moments from the recent matches.

**Setting Up the Environment:**

Before we dive into the analysis, let's make sure our R environment is ready. If you haven't already, make sure to install the necessary packages. We'll be using `tidyverse` for data manipulation and visualization, and `sportsdataverse` for fetching Premier League data.

```R

# Install and load required packages

install.packages("tidyverse")

install.packages("sportsdataverse")

library(tidyverse)

library(sportsdataverse)

```

**Fetching Data:**

Let's start by fetching the data for Game Week 2 using the `sportsdataverse` package. We'll focus on metrics like possession, shots on target, goals scored, and more.

```R

# Fetch Premier League data for Game Week 2

epl_data <- sdv_seasons() %>%

  filter(competition == "Premier League", season_slug == "2023-2024") %>%

  sdv_matches(round = 2)

# Display the first few rows of the data

head(epl_data)

```

**Analyzing Possession:**

Possession is a crucial aspect of football. Let's visualize the possession statistics for the teams in Game Week 2.

```R

# Visualize possession stats

possession_plot <- epl_data %>%

  ggplot(aes(x = team_name, y = possession)) +

  geom_bar(stat = "identity", fill = "blue") +

  labs(title = "Possession Statistics - Game Week 2",

       x = "Team", y = "Possession (%)") +

  theme_minimal() +

  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Display the possession plot

print(possession_plot)

```

**Shots Analysis:**

Shots on target often lead to goals. Let's analyze the shots and shots on target statistics for each team.

```R

# Shots and shots on target analysis

shots_plot <- epl_data %>%

  ggplot(aes(x = team_name, y = shots, fill = shots_on_target)) +

  geom_col(position = "dodge") +

  labs(title = "Shots and Shots on Target - Game Week 2",

       x = "Team", y = "Count", fill = "Shots on Target") +

  theme_minimal() +

  theme(legend.position = "top")

# Display the shots plot

print(shots_plot)

```

**Top Performers:**

Let's identify the top goal scorers from Game Week 2 and visualize their impact.

```R

# Identify top goal scorers

top_scorers <- epl_data %>%

  select(player_name, team_name, goals) %>%

  arrange(desc(goals)) %>%

  head(5)

# Visualize top goal scorers

top_scorers_plot <- top_scorers %>%

  ggplot(aes(x = reorder(player_name, goals), y = goals, fill = team_name)) +

  geom_bar(stat = "identity") +

  labs(title = "Top Goal Scorers - Game Week 2",

       x = "Player", y = "Goals", fill = "Team") +

  theme_minimal() +

  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Display the top scorers plot

print(top_scorers_plot)

```

**Conclusion:**

There you have it – a comprehensive analysis of Game Week 2 in the English Premier League using R code. We've explored possession statistics, shots analysis, and even highlighted the top goal scorers. Sports analytics not only enhances our understanding of the game but also adds an exciting dimension to the way we experience it. Stay tuned for more analysis as the Premier League season unfolds!

This work is licensed under a Creative Commons Attribution 4.0 International License.